Choosing an optimal β factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces

• Main Authors: Vogl, Teresa, Hrdina, Amy, Thomas, Christoph K.
• Format: Text
• Language: English
• Published: University of Bayreuth 2021
• Subjects: Turbulence; forest; greenhouse gas; climate change; relaxed eddy accumulation; atmospheric flux; Tive; Antarc*; Antarctica
• Online Access: https://dx.doi.org/10.15495/epub_ubt_00005783; https://epub.uni-bayreuth.de/id/eprint/5783

Accurately measuring the turbulent transport of re- active and conservative greenhouse gases, heat, and organic compounds between the surface and the atmosphere is crit- ical for understanding trace gas exchange and its response to changes in climate and anthropogenic activities. The re- laxed eddy accumulation (REA) method enables measuring the land surface exchange when fast-response sensors are not available, broadening the suite of trace gases that can be in- vestigated. The β factor scales the concentration differences to the flux, and its choice is central to successfully using REA. Deadbands are used to select only certain turbulent motions to compute the flux. This study evaluates a variety of different REA approaches with the goal of formulating recommendations applicable over a wide range of surfaces and meteorological conditions for an optimal choice of the β factor in combination with a suitable deadband. Observations were collected across three contrasting ecosystems offering stark differences in scalar transport and dynamics: a mid-latitude grassland ecosys- tem in Europe, a loose gravel surface of the Dry Valleys of Antarctica, and a spruce forest site in the European mid- range mountains. We tested a total of four different REA models for the β factor: the first two methods, referred to as model 1 and model 2, derive βp based on a proxy p for which high-frequency observations are available (sensible heat Ts). In the first case, a linear deadband is applied, while in the second case, we are using a hyperbolic deadband. The third method, model 3, employs the approach first published by Baker et al. (1992), which computes βw solely based upon the vertical wind statistics. The fourth method, model 4, uses a constant βp, const derived from long-term averaging of the proxy-based βp factor. Each β model was optimized with re- spect to deadband size before intercomparison. To our best knowledge, this is the first study intercomparing these differ- ent approaches over a range of different sites. With respect to overall REA performance, we found that the βw and constant βp, const performed more robustly than the dynamic proxy-dependent approaches. The latter mod- els still performed well when scalar similarity between the proxy (here Ts) and the scalar of interest (here water va- por) showed strong statistical correlation, i.e., during peri- ods when the distribution and temporal behavior of sources and sinks were similar. Concerning the sensitivity of the dif- ferent β factors to atmospheric stability, we observed that βT slightly increased with increasing stability parameter z/L when no deadband is applied, but this trend vanished with in- creasing deadband size. βw was unrelated to dynamic stabil- ity and displayed a generally low variability across all sites, suggesting that βw can be considered a site-independent con- stant. To explain why the βw approach seems to be insensi- tive towards changes in atmospheric stability, we separated the contribution of w? kurtosis to the flux uncertainty. For REA applications without deeper site-specific knowl- edge of the turbulent transport and degree of scalar similarity, we recommend using either the βp, const or βw models when the uncertainty of the REA flux quantification is not limited by the detection limit of the instrument. For conditions when REA sampling differences are close to the instrument’s de- tection limit, the βp models using a hyperbolic deadband are the recommended choice.